{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from simulation_data import *\n",
    "from scipy import special\n",
    "import pandas as pd\n",
    "import threading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mean(x):\n",
    "    return x.mean(axis=-1).mean(axis=-1)\n",
    "def get_biatt(x):\n",
    "    return x.mean(axis=-1).mean(axis=-1)>0.5\n",
    "def get_bias(var1,true_var):\n",
    "    return (var1-true_var).mean()\n",
    "def get_rmse(var1,true_var):\n",
    "    return np.power(np.power(var1-true_var,2),0.5).mean()\n",
    "class MyThread(threading.Thread):  \n",
    "    def __init__(self, func, args=()):  \n",
    "        super(MyThread, self).__init__()  \n",
    "        self.func = func  \n",
    "        self.args = args  \n",
    "  \n",
    "    def run(self):  \n",
    "        self.result = self.func(*self.args)\n",
    "def get_result(self):  \n",
    "    try:  \n",
    "        return self.result  \n",
    "    except Exception as e:  \n",
    "        return None  \n",
    "def stage_2(_):\n",
    "    return _[t_data[\"flag\"].sum(axis=1)>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 500\n",
    "i = I = 30\n",
    "k = 4\n",
    "s = 0.1\n",
    "g = 0.1\n",
    "student_mean = [0,0]\n",
    "student_cov = [[1,0.25],[0.25,0.25]]\n",
    "lambda_k = np.repeat(1.5,k)\n",
    "lambda_0 = np.linspace(-1,0.5,k)\n",
    "\n",
    "beta_i = special.logit(g)\n",
    "delta_i = special.logit(1 - s) - special.logit(g)\n",
    "item_mean = [beta_i,delta_i,4]\n",
    "item_cov = [[1,-0.8,-0.25],\n",
    "            [-0.8,1,0.15],\n",
    "            [-0.25,0.15,0.25]]\n",
    "omega = [1,5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "student = CdmStudentParam(n,k,student_mean,\n",
    "                        student_cov,lambda_k,lambda_0,\n",
    "                        )\n",
    "student.get()\n",
    "item = CdmBaseModel(i,k,item_mean,item_cov,omega)\n",
    "item.get()\n",
    "compute_model = CdmJointModelCompute(i,k,n,item,student)\n",
    "compute_model.get()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "abberant_flag = Abberant(n,i,student,item,compute_model,\n",
    "        item.g,0.9,aberrant_rt_mean=2,aberrant_rt_var=0.25)\n",
    "l = np.array([25,26,27,28,29])\n",
    "abberant_flag.random_preknowledge([25,26,27,28,29],0.2)\n",
    "# abberant_flag.random_rg(0.15,0.4)\n",
    "abberant_flag.time_limit(3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "快速猜测：\n",
      " [0.    0.    0.    0.    0.    0.    0.    0.    0.    0.002 0.004 0.008\n",
      " 0.028 0.03  0.044 0.044 0.052 0.06  0.068 0.068 0.084 0.086 0.094 0.104\n",
      " 0.122 0.108 0.106 0.11  0.114 0.122]\n",
      "预先知识：\n",
      " [0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      " 0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      " 0.    0.176 0.182 0.206 0.18  0.192]\n",
      "总计：\n",
      " [0.    0.    0.    0.    0.    0.    0.    0.    0.    0.002 0.004 0.008\n",
      " 0.028 0.03  0.044 0.044 0.052 0.06  0.068 0.068 0.084 0.086 0.094 0.104\n",
      " 0.122 0.284 0.288 0.316 0.294 0.314]\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "print(\"快速猜测：\\n\",(abberant_flag.flag==1).mean(axis=0))\n",
    "print(\"预先知识：\\n\",(abberant_flag.flag==2).mean(axis=0))\n",
    "print(\"总计：\\n\",(abberant_flag.flag>0).mean(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_key = [\"student_cov\",\"lambda_0\",\"lambda_k\",\"item_mean\",\"item_cov\",\n",
    "            \"omega\",\"xi\",\"theta\",\"tau\",\"att\",\"beta_i\",\"delta_i\",\"d_c\",\"d_g\",\"mu_c\",\"var_c\",\"flag\"]\n",
    "t_data_list = [student_cov,lambda_0,lambda_k,\n",
    "            item_mean,item_cov,\n",
    "            item.alpha,item.xi,student.theta,student.tau,\n",
    "            student.specific_att,item.beta_i,item.delta_i,\n",
    "            abberant_flag.d_c,abberant_flag.d_g,2,0.25,abberant_flag.flag]\n",
    "model1_key = data_key\n",
    "\n",
    "model2_key = [i for i in data_key if i != \"d_g\"]\n",
    "t_data = {data_key[i]:t_data_list[i] for i in range(len(data_key))}\n",
    "model1_key.append(\"flag_g_c\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-1ec11e4e746f>:1: RuntimeWarning: invalid value encountered in true_divide\n",
      "  xxx = (t_data[\"flag\"]==1).sum(axis=1)/((t_data[\"flag\"]==1).sum(axis=1)+(t_data[\"flag\"]==2).sum(axis=1))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.20506557148922075"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xxx = (t_data[\"flag\"]==1).sum(axis=1)/((t_data[\"flag\"]==1).sum(axis=1)+(t_data[\"flag\"]==2).sum(axis=1))\n",
    "xxx = stage_2(xxx)\n",
    "xxx.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='x', ylabel='Count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WAkuBGcBvzGyhuw/2+8kwoVLZIumX6uqjS+7+j+6+w913dX0NdJGZlQJ/TDyZdLkL2BQ83wTcndD+nLtfdPdaoAa4OcX4ZBhSqWyR9Eu1p/CvZvbfgeeBi12N7j7Q/8CngL+i+yT1dHdvCK5vMLPioH0m8EbCebGgrRszWw+sB5g9e3aK4cu1SqWyRdIr1aSwLnj8y4Q2B+b1dYGZfRpodPddZvaJFL6H9dLmPRrcNwIbAcrLy3u8LiIiVy6lpODuc6/gvW8B1pjZnUAhMMHM/gk4bmYlQS+hBGgMzo8BsxKuLwWOXcH3FRGRK5RSUjCz+3trd/ef9nWNuz8GPBZc/wngf7r7583sb4j3PJ4MHrcGl2wDfm5m3yE+0bwA2JHSTyEiImmR6vDRqoTnhcBqYDfQZ1Lox5PAZjN7CDgC3APg7vvMbDNQBVwCNmjlkYjI0Ep1+OjLicdmNhH4WarfxN1fBl4Onp8knlR6O+8J4IlU31dERNIr1Z5CsnPEh3dEJIuo7IVcrVTnFP6VD1YC5QGLgc1RBSUiV0ZlL+RqpdpT+NuE55eAOnePRRCPiFwllb2Qq5HSjuagMN67xDehTQbaowxKREQyI9Xhoz8F/ob4ZLEBf29mf+nuWyKMTaQb1ToSiV6qw0ffBFa5eyOAmRUBvwGUFGTIdNU6WrwwvsahunIPE27SMIlIOqWaFEZ0JYTASVIvpieSNqp1JBKtVJPCC2b278CzwfF/AX4VTUgiIpIpA92jeT7xqqZ/aWafAW4lPqfwOvDMEMQnIiJDaKAhoKeAVgB3/4W7P+zu/4N4L+GpaEMTEZGhNlBSKHP3yuRGd68gfjtNERG5hgyUFAr7eW10OgMREZHMGygp7DSz/5bcGFQ4HfB2nCIiklsGWn30NeB5M7uPD5JAOVAA/EmEcYmISAb0mxTc/TjwMTP7JHBD0PxLd//PyCMTEZEhl+r9FLYD2yOORUTSTKW0ZbCu9H4KIpIDVEpbBktJQeQap1LaMhiqXyQiIqHIkoKZFZrZDjPbY2b7zOzxoH2Kmb1oZgeCx8kJ1zxmZjVmVm1mt0cVm4iI9C7KnsJF4FPuvhxYAdxhZh8FHgVecvcFwEvBMWa2BFgLLAXuAJ42s7wI4xMRkSSRJQWPawsO84MvB+4CNgXtm4C7g+d3Ac+5+0V3rwVqgJujik9ERHqKdE7BzPLM7G2gEXjR3d8kXnW1ASB4LA5OnwkcTbg8FrQlv+d6M6sws4qmpqYowxcRGXYiTQru3unuK4BS4GYzu6Gf0623t+jlPTe6e7m7lxcVFaUpUhERgSFakuruzWb2MvG5guNmVuLuDWZWQrwXAfGewayEy0qBY0MRn2Qn3ZM5esm/Y9DmtuEuytVHRWY2KXg+GvgD4F1gG7AuOG0dsDV4vg1Ya2ajzGwusADYEVV8kv267sl84UQjF040Ul25h9OnT2U6rGtK8u/4rdde7ZEkZHiJsqdQAmwKVhCNADa7+7+Z2evA5qDS6hHgHgB332dmm4Eq4BKwwd07I4xPcoDuyZxeyWUvqqqqWDj/Om1uk1BkSSG4Oc9NvbSfBFb3cc0TwBNRxSQy3CWXvaiu3MOEm5QQ5AMqcyEyzCSWvVDvS5KpzIWIiISUFEREJKSkICIiIc0pSNbQvgSRzFNPQbKG9iWIZJ56CpJVtC9BJLPUUxARkZCSgoiIhDR8JCKh5DIYoAJ5w42SgoiEkstg7A/mdcrLyzMZlgwhJQUR6SaxDIYMP5pTEBGRkJKCiIiElBRERCSkpCAiIiElBRERCSkpiIhISEtSJWNUFVUk+0TWUzCzWWa23cz2m9k+M/tq0D7FzF40swPB4+SEax4zsxozqzaz26OKTbKDqqKKZJ8oh48uAV9398XAR4ENZrYEeBR4yd0XAC8FxwSvrQWWAncAT5tZXoTxSRboqoq6csVy5s6enelwRIa9yJKCuze4++7geSuwH5gJ3AVsCk7bBNwdPL8LeM7dL7p7LVAD3BxVfCIi0tOQzCmYWRlwE/AmMN3dGyCeOMysODhtJvBGwmWxoC35vdYD6wFm6y/LnKI5BJHsF/nqIzMbB/wL8DV3b+nv1F7avEeD+0Z3L3f38qKionSFKUNAcwgi2S/SnoKZ5RNPCM+4+y+C5uNmVhL0EkqAxqA9BsxKuLwUOBZlfDL0dGc1kewW5eojA34I7Hf37yS8tA1YFzxfB2xNaF9rZqPMbC6wANgRVXwiItJTlD2FW4A/A94xs7eDtm8ATwKbzewh4AhwD4C77zOzzUAV8ZVLG9y9M8L4REQkSWRJwd1/R+/zBACr+7jmCeCJqGISEZH+qcyFiIiElBRERCSk2kci0qeOjg4OVVX1aF+2bBkFBQUZiEiipqQgIn06WHuY5jPNzCueFrbtD5YSl5eXZyosiZCSgoj0a/68ueHeErn2aU5BRERCSgoiIhJSUhARkZCSgoiIhDTRLJFRqWyR3KOegkRGpbJFco96ChIplcoWyS3qKYiISEg9BREZlN5KX6jsxbVDSUFEBiW59IXKXlxblBREZNBU+uLapTkFEREJqacgIldFcwzXFiUFSRttVhueNMdwbYls+MjMfmRmjWa2N6Ftipm9aGYHgsfJCa89ZmY1ZlZtZrdHFZdER5vVhq+uOYaVK5azeOGCTIcjVyHKOYWfAHcktT0KvOTuC4CXgmPMbAmwFlgaXPO0meVFGJtEpGuz2soVy5k7e3amwxGRQYosKbj7K0Dyn4l3AZuC55uAuxPan3P3i+5eC9QAN0cVm4iI9G6oVx9Nd/cGgOCxOGifCRxNOC8WtImIyBDKliWp1kub93qi2XozqzCziqampojDEhEZXoZ69dFxMytx9wYzKwEag/YYMCvhvFLgWG9v4O4bgY0A5eXlvSYOEckcLVHNbUPdU9gGrAuerwO2JrSvNbNRZjYXWADsGOLYRCQNDtYeprGuNlyF9tZrr3ZbqizZLbKegpk9C3wCmGZmMeBbwJPAZjN7CDgC3APg7vvMbDNQBVwCNrh7Z1SxiUi0VAYjd0WWFNz93j5eWt3H+U8AT0QVj4iIDCxbJppFRCQLKCmIiEhISUFEREIqiCdXTAXwRK49SgpyxboK4HUVQKuu3MOEm7TiRPqX/McEaB9DNlFSkJT11jNYOP+6cOnhu0HJZJFEyZvZqqqqOH/6JEsXLQJUajvbKClIytQzkCuRfL+F6so9rLppufYxZCklhWHiSrrsg+0ZdPplTp8+TUNDAwDNZ5qZPGliun4EyWGJm9nUo8xuSgo5aqAP+d4+0I/seo8Fc+YBUH34IFXlVSxZsiQ859y5c9TU1DByZPyfxYEDBzhefZSymfH7Iuyu2sO99/1JnzG1trTQ2XaBU3XHAWg82sDUyZOu/ocVkSGjpJCjKisr+Y9ntnHdrDKg54d8chJ4o+I1lpQtYOmCxQAcPHqYd7bvZNSJS+F7bn7h/1JSPJkbr78+/h47djKrZAY3Lo6/x+GGGlpazvQb19jRYyieEh8mGDN6TPp+YBEZEkoKOey6WWV9fsi/vuN3zJw8nclj48M340aN5vLl7kVl55SUhtcDjN3+AiVTilg4M54EKibs4UPTilh2ffycir17aGlp1fCQyDVMSeEakvghv+udtzhYU8u4vAkAHKw5zITCcQO+R97IPApHFQIwYkT3vY3nL5yn5cTpcHjo/SP15OflhUmipbWFUaOVJERymZJCjuhtjmDk5cv9XlNYOJpJEycBMCr4oO9y+fJlmk83c+zYB7etOHv23IBxjCkcHQ4PjbAR3ZLE6fdPMrF0dEo/j4hkJyWFHJE8h9A1R5Ayd1paW8MkUB+LcaHlQtiTAGg83kRnZ9+Jxt3paO+gtbUVgPaOdsaMmhYmidGFhX1eCz1XJ508fQrDNRw1zOmmPNlFSSFLpLJkNHEO4UDdoW5/6Tc3n2bcqL4ndi+2X+RYrIGqd94D4MiRGHl5eZw+98HE8fmOi7h3TwqXLl0Kk8D58+c5O7KApsaTAJxtPcv5iee7JYneb6Ial7w6KXawjvZpbZyardVKw1nyPoZ3qvZTVdV9ZZySxNBRUsgSyT2Bg0cPA33v8mxpOUNTrKnPOYPOzk4am09Q21AHwOnWZsYVzQiHk853XOT668qYM68ovKa0rpj2S+3h8eXLlzl//kKYBM6fv8CUCRPDOQfMur1+tu0cHR0d4fXu3m1iuqW1haLRE7v1LBKHo7RaafhK3sfQWFcbJgnteB5aSgpZJLEncLG9nV/+8pdUBd3q2tpariv8ULfz+5szaDp9gpJZU8IP/aNnPkRNdR1lQZJoOdfKjOJiFs+bH17z+p6dPWLKyxvR58Rzb68n9izOtJ5hZN4IzTnIoCUmiY6OjvD/QdcxQH5+ftimnkT6KClkqb3V+9hfVc2ZOY0AvF29h7M3rqB4Uvyvp9aWVjxxqCZpzuDs2XMsWTA3/NB/fc9OFl4/O0wSyb2CdEjuWZxtPcu0iZMZnT8KgLwRI2hvb+97uMnRklfpIXl46de/eYnJEyfw0VWrAPUk0k1JIUOS5xAqKytpa2wM9xW0trQys2gGyxbcAEBdw5FucwKx2DGKggQBPecMeps0nlE8vVuSiEJizyF5eKm1tY2xo0b3OdzU3nGR+tgx9r1TDcDBA7WaYxCg5/DStKmT++xJgHoOV0NJYYgMVHZi+/YXGHlhBJfb4n86J3/oAxSMGhUOFxUUjOr22uXLl2npaAsnjnubNM6E5OGl/oabLl7sIG/UB/8kO/0yDQ3vh7+3I7EjTJo4foh/Asl2g52oVunu/mVdUjCzO4DvAnnAD9z9yQyHdEXa2tr4xS9+ER7X1NTQVnuCebPmAPDGnl0sLJ37wY7jgjG4E37ojxyZz4kzJ8OJ4pNnTnGh/UKfx0eb6lm1cmmkw0Ppljzc1HymmWlTJjPyQicA7W1naX7/JO+/FwPg2KEYowtG0bBsGdBzSetAxxqOunb1N1GdnCRUurt/WZUUzCwP+B5wGxADdprZNnev6v/K9Bvor4nkD/0LFy4AUBis1f/tb3/LoYp3KSmaAcDeg/somVQMFw2ITxw3HWuk+XQbAJX79zFp/CSm9/EhP6luLNMmT+rzuLSumKIpUyIfHkq35J7DqPwCJo+Lf3Dnj8yncNQopk8pDs7N4+ypFmL7DwFw5L1a3h9dz+iR8ev3Ve5l7oyZxKbFf+e11TW0T/9QuOT1+JFj3XZgK0lcu/pLEsmluwcafkr+LEhlojuXeyNZlRSAm4Eadz8EYGbPAXcBkSSFxx9/vM/X6uvrOfzOASYHf7mfPtNM2Y0LmDlzJgBvv/02NW/tZ2LwAVbf1MCokQVMmzwVgEPH6li+9HomTIsP84xuKsDHXOLciPjwzqX8jh7HHSPPc/xc/K/ivNHQdv4cp1pOAdBxqZ2LHR39HtfWx3j17R0AHGt8nwvtF1M+vpJrhvo49v4xzp4/y2s7KwCoPVrHhAkTeG9/fA7i+PFGLndcojBIEkfr62nv6GD33vh/zrpYjLqGeo6/H0ze793L3LI5YZG/N3buZNKE8Zw+fTr8nSS3DffjbIjhSo8P1BwI/p0c53evvR6+/uqbO2lpbWFvRbwa8PuNjRROmBT+X6+vr+dCSzMfKo7/cbLv3XcZO2YMZbN7P7+3a3o752p961vfStt7JTLvtoQls8zsc8Ad7v5fg+M/Az7i7l9KOGc9sD44XARUA9OAE0Mc7pVQnOmlONNLcaZXNsc5x92Lensh23oK1ktbt6zl7huBjd0uMqtw96wfEFSc6aU400txpleuxJms526kzIoBsxKOS4FjfZwrIiJplm1JYSewwMzmmlkBsBbYluGYRESGjawaPnL3S2b2JeDfiS9J/ZG770vh0o0Dn5IVFGd6Kc70UpzplStxdpNVE80iIpJZ2TZ8JCIiGaSkICIioZxOCmZ2h5lVm1mNmT2a6Xj6YmY/MrNGM9ub6Vj6YmazzGy7me03s31m9tVMx9QbMys0sx1mtieIs+8diFnAzPLM7C0z+7dMx9IfMztsZu+Y2dtmVpHpePpiZpPMbIuZvRv8W/29TMeUzMwWBb/Hrq8WM/tapuNKVc7OKQQlMd4joSQGcG8mSmIMxMw+DrQBP3X3GzIdT2/MrAQocffdZjYe2AXcnW2/TzMzYKy7t5lZPvA74Kvu/kaGQ+uVmT0MlAMT3P3TmY6nL2Z2GCh392zdbAWAmW0C/p+7/yBYoTjG3ZszHFafgs+peuKbcOsyHU8qcrmnEJbEcPd2oKskRtZx91eAU5mOoz/u3uDuu4PnrcB+IH178tPE49qCw/zgKyv/sjGzUuCPgR9kOpZrgZlNAD4O/BDA3duzOSEEVgMHcyUhQG4nhZnA0YTjGFn4IZaLzKwMuAl4M8Oh9CoYknkbaARedPesjBN4CvgrIPM1zAfmwH+Y2a6glEw2mgc0AT8OhuR+YGZjMx3UANYCz2Y6iMHI5aQwYEkMGTwzGwf8C/A1d2/JdDy9cfdOd19BfMf7zWaWdUNyZvZpoNHdd2U6lhTd4u4rgT8CNgRDntlmJLAS+Ed3vwk4C2TzXGIBsAb450zHMhi5nBRUEiPNgjH6fwGecfdfDHR+pgVDBy8Dd2Q2kl7dAqwJxuqfAz5lZv+U2ZD65u7HgsdG4Hniw7PZJgbEEnqGW4gniWz1R8Budz+e6UAGI5eTgkpipFEwgftDYL+7fyfT8fTFzIrMbFLwfDTwB8C7GQ2qF+7+mLuXunsZ8X+b/+nun89wWL0ys7HB4gKC4Zg/BLJupZy7vw8cNbNFQdNqIiqrnyb3kmNDR5BlZS4G4ypKYgw5M3sW+AQwzcxiwLfc/YeZjaqHW4A/A94JxusBvuHuv8pcSL0qATYFqzpGAJvdPauXe+aA6cDz8b8LGAn83N1fyGxIffoy8Ezwh+Ah4M8zHE+vzGwM8ZWRf5HpWAYrZ5ekiohI+uXy8JGIiKSZkoKIiISUFEREJKSkICIiISUFEREJKSmIiEhISUFEREJKCiJpZGarzKwyuO/D2OCeD1lXm0mkL9q8JpJmZva/gEJgNPFaPf87wyGJpExJQSTNghIMO4ELwMfcvTPDIYmkTMNHIuk3BRgHjCfeYxDJGeopiKSZmW0jXi57LvFbnH4pwyGJpCxnq6SKZCMzux+45O4/Dyq5vmZmn3L3/8x0bCKpUE9BRERCmlMQEZGQkoKIiISUFEREJKSkICIiISUFEREJKSmIiEhISUFEREL/H50iDX5C7GchAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "a = np.log(abberant_flag.rt.flatten())\n",
    "b = abberant_flag.flag.flatten()\n",
    "data = pd.DataFrame(np.vstack((a,b)),index=[\"x\",\"label\"]).T\n",
    "sns.histplot(data=data,x=\"x\",hue=\"label\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abberant_flag.aberrant_rt_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from param_estimate_code import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = [25,26,27,28,29]\n",
    "l_ = []\n",
    "for i in range(30):\n",
    "    if i in l:\n",
    "        l_.append(1)\n",
    "    else:\n",
    "        l_.append(0)\n",
    "l = np.array(l_)\n",
    "import pyjags \n",
    "from param_estimate_code import *\n",
    "input_data = {\n",
    "    \"I\":I,\n",
    "    \"N\":n,\n",
    "    \"K\":k,\n",
    "    \"Q\":compute_model.Q,\n",
    "    \"Y\":abberant_flag.ra,\n",
    "    \"logT\":np.log(abberant_flag.rt),\n",
    "    \"R1\":np.eye(3),\n",
    "    \"R2\":np.eye(2)\n",
    "}\n",
    "_ = input_data.copy()\n",
    "_[\"item_gate\"] = l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "burning.......\n",
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      "burning.......\n",
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      "sampling: iterations 933 of 1000, elapsed 0:02:11, remaining 0:00:09\n",
      "sampling: iterations 983 of 1000, elapsed 0:02:18, remaining 0:00:02\n",
      "sampling: iterations 1000 of 1000, elapsed 0:02:22, remaining 0:00:00\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def model_sampler(model_code,return_vars,input_data):\n",
    "  model = pyjags.Model(code=model_code,\n",
    "                     data=input_data,\n",
    "                     chains=2,\n",
    "                     progress_bar=True,\n",
    "                     threads=4,\n",
    "                      generate_data=True,\n",
    "                      chains_per_thread=1,\n",
    "                    adapt=False\n",
    "                     )\n",
    "  print(\"burning.......\")\n",
    "  model.sample(iterations=3000,vars=[],thin=1)\n",
    "\n",
    "  print(\"sampling.......\")\n",
    "  jags_tarce_posterior = model.sample(iterations=500,vars=return_vars,thin=1)\n",
    "  return jags_tarce_posterior\n",
    "# .append(\"item_gate\")\n",
    "t1 = MyThread(model_sampler,(model5,model1_key,_))\n",
    "t2 = MyThread(model_sampler,(model2,model2_key,input_data))\n",
    "t1.start()\n",
    "t2.start()\n",
    "t1.join()\n",
    "t2.join()\n",
    "model1_trace = get_result(t1)\n",
    "model2_trace = get_result(t2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle \n",
    "def save(path,x):\n",
    "    with open (f\"./{path}.pkl\",\"wb\") as f:\n",
    "        pickle.dump(x,f)\n",
    "def load(path,x):\n",
    "    with open (f\"./{path}.pkl\",\"wb\") as f:\n",
    "        _ = pickle.load(f)\n",
    "    return _\n",
    "save(\"model1_trace\",model1_trace)\n",
    "save(\"model2_trace\",model2_trace)\n",
    "save(\"true_data\",t_data)\n",
    "save(\"input_data\",input_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_1_param = {\n",
    "    key:get_mean(model1_trace[key]) for key in model1_key if key not in  [\"att\",\"flag\",\"flag_g_c\",\"item_gate\"] \n",
    "}\n",
    "model_2_param = {\n",
    "    key:get_mean(model2_trace[key]) for key in model2_key if key not in  [\"att\",\"flag\"] \n",
    "}\n",
    "model1_att = get_biatt(model1_trace[\"att\"])\n",
    "model2_att = get_biatt(model2_trace[\"att\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# {(k,model_1_param[k].shape) for k in model_1_param.keys()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model1_key = model1_key.remove(\"item_gate\")\n",
    "model1_bias = {(\"bias_\"+key):get_bias(model_1_param[key],t_data[key]) for key in model_1_param.keys()}\n",
    "model2_bias = {(\"bias_\"+key):get_bias(model_2_param[key],t_data[key]) for key in model_2_param.keys()}\n",
    "model1_rmse = {(\"rmse_\"+key):get_rmse(model_1_param[key],t_data[key]) for key in model_1_param.keys()}\n",
    "model2_rmse = {(\"rmse_\"+key):get_rmse(model_2_param[key],t_data[key]) for key in model_2_param.keys()}\n",
    "df1,df2,df3,df4 = pd.DataFrame(model1_bias,columns=[\"model1\"]),\\\n",
    "                    pd.DataFrame(model2_bias,columns=[\"model2\"]),\\\n",
    "                        pd.DataFrame(model1_rmse,columns=[\"model1\"]),\\\n",
    "                            pd.DataFrame(model1_rmse,columns=[\"model2\"])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'item_gate'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-9978ccc72247>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0ml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmodel1_trace\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"item_gate\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'item_gate'"
     ]
    }
   ],
   "source": [
    "l = 0.1*l\n",
    "plt.bar([i for i in range(30)],model1_trace[\"item_gate\"].mean(axis=-1).mean(axis=-1))\n",
    "plt.bar([i for i in range(30)],l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'mymodel')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = ((model1_trace[\"flag\"]==3).mean(axis=-1).mean(axis=-1)*model_1_param[\"d_c\"]).sum(axis=0)\n",
    "# _ = (model1_trace[\"flag\"]==3).mean(axis=-1).mean(axis=-1).sum(axis=0)\n",
    "plt.bar([i for i in range(1,31)],_)\n",
    "plt.bar([i for i in range(1,31)],l)\n",
    "# plt.plot([i for i in range(1,31)],np.repeat(min(np.where((_*l)==0,999,(_*l))),30))\n",
    "plt.title(\"mymodel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7e580c0c40>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = (model2_trace[\"flag\"].mean(axis=-1).mean(axis=-1)*model_2_param[\"d_c\"]).sum(axis=0)\n",
    "plt.bar([i for i in range(1,31)],_)\n",
    "plt.bar([i for i in range(1,31)],l)\n",
    "plt.plot([i for i in range(1,31)],np.repeat(min(np.where((_*l)==0,999,(_*l))),30))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7e51109c10>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = model_1_param[\"d_c\"]\n",
    "plt.bar([i for i in range(1,31)],_)\n",
    "plt.bar([i for i in range(1,31)],np.array(l)*0.01)\n",
    "plt.plot([i for i in range(1,31)],np.repeat(min(np.where((_*l)==0,999,(_*l))),30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7e510152e0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = model_2_param[\"d_c\"]\n",
    "plt.bar([i for i in range(1,31)],_)\n",
    "plt.bar([i for i in range(1,31)],np.array(l)*0.01)\n",
    "plt.plot([i for i in range(1,31)],np.repeat(min(np.where((_*l)==0,999,(_*l))),30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9,\n",
       "       0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9,\n",
       "       0.9, 0.9, 0.9, 0.9])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1_param[\"d_c\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,\n",
       "       0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.1,\n",
       "       0.1, 0.1, 0.1, 0.1])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 30 artists>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar([i for i in range(I)],model_2_param[\"d_c\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13367,    55],\n",
       "       [   13,  1565]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score,confusion_matrix,ConfusionMatrixDisplay\n",
    "def stage_2(_):\n",
    "    return _[t_data[\"flag\"].sum(axis=1)>0]\n",
    "confusion_matrix(t_data[\"flag\"].flatten()>0,get_mean(model2_trace[\"flag\"]).flatten()>0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13360,    62],\n",
       "       [   10,  1568]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(t_data[\"flag\"].flatten()>0,(get_mean(model1_trace[\"flag\"]>1)>0.5).flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f7e51076190>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pre_flag = (get_mean(model1_trace[\"flag_g_c\"])>0.5)*(get_mean(model1_trace[\"flag\"]>1)>0.5)*1+(get_mean(model1_trace[\"flag_g_c\"])<0.5)*(get_mean(model1_trace[\"flag\"]>1)>0.5)*2\n",
    "\n",
    "# cohen_kappa_score(t_data[\"flag\"].flatten(),pre_flag.flatten())\n",
    "_ = confusion_matrix(t_data[\"flag\"].flatten(),pre_flag.flatten())\n",
    "_ = ConfusionMatrixDisplay(_)\n",
    "_.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-34-669e1fa6f452>:4: RuntimeWarning: invalid value encountered in true_divide\n",
      "  yyy = _.sum(axis=-1)/(_>0).sum(axis=-1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[140,   6],\n",
       "       [174,  77]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xxx = (t_data[\"flag\"]==1).sum(axis=1)<(t_data[\"flag\"]==2).sum(axis=1)\n",
    "# yyy =((pre_flag==1).sum(axis=1))<((pre_flag==2).sum(axis=1))\n",
    "_ = get_mean(model1_trace[\"flag_g_c\"])*(get_mean(model1_trace[\"flag\"]>1)>0.5)\n",
    "yyy = _.sum(axis=-1)/(_>0).sum(axis=-1)\n",
    "resp_flag_t = xxx[t_data[\"flag\"].sum(axis=1)>0]\n",
    "resp_flag_p = (yyy<0.5)[t_data[\"flag\"].sum(axis=1)>0]\n",
    "confusion_matrix(resp_flag_t.flatten(),resp_flag_p.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 99,  47],\n",
       "       [121, 130]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_ = (model2_att.reshape(500,1,4)>=compute_model.Q.reshape(1,30,4)).prod(axis=-1)\n",
    "_ = model_2_param[\"beta_i\"]+model_2_param[\"delta_i\"]*_\n",
    "_ = np.exp(_)/(1+np.exp(_))\n",
    "v = ((abberant_flag.ra-_)/np.power(_*(1-_),0.5)).mean(axis=1)\n",
    "confusion_matrix(resp_flag_t.flatten(),v[t_data[\"flag\"].sum(axis=1)>0].flatten()>0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stage_2((t_data[\"flag\"]==1).sum(axis=1)/((t_data[\"flag\"]==1).sum(axis=1)+(t_data[\"flag\"]==2).sum(axis=1)))[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stage_2((t_data[\"flag\"]==1).sum(axis=1)+(t_data[\"flag\"]==2).sum(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='idx', ylabel='value'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xxx = (t_data[\"flag\"]==1).sum(axis=1)>(t_data[\"flag\"]==2).sum(axis=1)\n",
    "_ = np.array([stage_2(np.array(i)) for i in [np.arange(n),xxx]])\n",
    "\n",
    "_ = pd.DataFrame(_.T,columns=[\"idx\",\"flag\"])\n",
    "_[\"value\"]=stage_2(yyy)\n",
    "_[\"cnt\"] = stage_2((t_data[\"flag\"]==1).sum(axis=1)+(t_data[\"flag\"]==2).sum(axis=1))\n",
    "# _ = _[_[\"cnt\"]>2]\n",
    "sns.scatterplot(data=_,x=\"idx\",y=\"value\",hue=\"flag\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='flag', ylabel='value'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=_,x=\"flag\",y=\"value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# xxx = (t_data[\"flag\"]==1).sum(axis=1)/((t_data[\"flag\"]==1).sum(axis=1)+(t_data[\"flag\"]==2).sum(axis=1))\n",
    "# _ = np.array([stage_2(np.array(i)) for i in [np.arange(n),get_mean(model1_trace[\"flag_g_c\"]),xxx]])\n",
    "\n",
    "# _ = pd.DataFrame(_.T,columns=[\"idx\",\"value\",\"flag\"])\n",
    "# _[\"cnt\"] = stage_2((t_data[\"flag\"]==1).sum(axis=1)+(t_data[\"flag\"]==2).sum(axis=1))\n",
    "# # _ = _[_[\"cnt\"]>2]\n",
    "# sns.scatterplot(data=_,x=\"flag\",y=\"value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='idx', ylabel='value'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xxx = (t_data[\"flag\"]==1).sum(axis=1)>(t_data[\"flag\"]==2).sum(axis=1)\n",
    "_ = np.array([stage_2(np.array(i)) for i in [np.arange(n),v,xxx]])\n",
    "_ = pd.DataFrame(_.T,columns=[\"idx\",\"value\",\"flag\"])\n",
    "\n",
    "sns.scatterplot(data=_,x=\"idx\",y=\"value\",hue=\"flag\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='flag', ylabel='value'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=_,x=\"flag\",y=\"value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-37-b9bb8a69c0d2>:1: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _ = (get_mean((model1_trace[\"flag\"]==2)*model_1_param[\"d_g\"].reshape(1,-1,1,1)+\\\n"
     ]
    }
   ],
   "source": [
    "_ = (get_mean((model1_trace[\"flag\"]==2)*model_1_param[\"d_g\"].reshape(1,-1,1,1)+\\\n",
    "    (model1_trace[\"flag\"]==3)*model_1_param[\"d_c\"].reshape(1,-1,1,1))).sum(axis=1)/get_mean(model1_trace[\"flag\"]>1).sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.054, 0.068, 0.058, 0.058, 0.042, 0.054, 0.058, 0.048, 0.05 ,\n",
       "       0.034, 0.058, 0.046, 0.006, 0.064, 0.048, 0.046, 0.04 , 0.054,\n",
       "       0.066, 0.054, 0.058, 0.056, 0.036, 0.034, 0.038, 0.258, 0.242,\n",
       "       0.178, 0.24 , 0.26 ])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(get_mean(model2_trace[\"flag\"])>0.5).mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.054, 0.072, 0.06 , 0.062, 0.062, 0.056, 0.058, 0.054, 0.058,\n",
       "       0.054, 0.056, 0.048, 0.066, 0.068, 0.048, 0.046, 0.062, 0.064,\n",
       "       0.07 , 0.056, 0.062, 0.066, 0.046, 0.05 , 0.042, 0.258, 0.244,\n",
       "       0.22 , 0.238, 0.258])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(t_data[\"flag\"]>0).mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rmse_student_cov': 0.005743882384930329,\n",
       " 'rmse_lambda_0': 0.023107161546996935,\n",
       " 'rmse_lambda_k': 0.10247177659079681,\n",
       " 'rmse_item_mean': -0.012486163507664155,\n",
       " 'rmse_item_cov': 0.07767026879040582,\n",
       " 'rmse_omega': -0.0016956009683937456,\n",
       " 'rmse_xi': -0.023649301268285548,\n",
       " 'rmse_theta': 0.0048835219725687495,\n",
       " 'rmse_tau': -0.006337433028060592,\n",
       " 'rmse_beta_i': 0.041247200737891565,\n",
       " 'rmse_delta_i': -0.030287711052548905,\n",
       " 'rmse_d_c': -0.5628936031417914,\n",
       " 'rmse_mu_c': 0.0014666030010148656,\n",
       " 'rmse_var_c': 4.4025857819790515e-05}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{key:model1_rmse[key]-model2_rmse[key] for key in model2_rmse.keys()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rmse_student_cov': 0.005743882384930329,\n",
       " 'rmse_lambda_0': 0.023107161546996935,\n",
       " 'rmse_lambda_k': 0.10247177659079681,\n",
       " 'rmse_item_mean': -0.012486163507664155,\n",
       " 'rmse_item_cov': 0.07767026879040582,\n",
       " 'rmse_omega': -0.0016956009683937456,\n",
       " 'rmse_xi': -0.023649301268285548,\n",
       " 'rmse_theta': 0.0048835219725687495,\n",
       " 'rmse_tau': -0.006337433028060592,\n",
       " 'rmse_beta_i': 0.041247200737891565,\n",
       " 'rmse_delta_i': -0.030287711052548905,\n",
       " 'rmse_d_c': -0.5628936031417914,\n",
       " 'rmse_mu_c': 0.0014666030010148656,\n",
       " 'rmse_var_c': 4.4025857819790515e-05}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{key:model1_rmse[key]-model2_rmse[key] for key in model2_rmse.keys()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # print(model_1_param[\"d_g\"])\n",
    "# # print(model_2_param[\"d_c\"])\n",
    "# # model_1_param[\"omega\"]\n",
    "# import arviz\n",
    "# ar_data1 = arviz.from_pyjags({key:model1_trace[key].reshape(-1,500,2) for key in model1_trace.keys()})\n",
    "# ar_data2 = arviz.from_pyjags({key:model2_trace[key].reshape(-1,500,2) for key in model2_trace.keys()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ar_data1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-43-fdefa41ec355>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0marviz\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0maz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mar_data1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdf2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mar_data2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ar_data1' is not defined"
     ]
    }
   ],
   "source": [
    "import arviz as az\n",
    "df1 = az.summary(ar_data1)\n",
    "df2 = az.summary(ar_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-54fa52b7da1f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"r_hat\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m1.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'df1' is not defined"
     ]
    }
   ],
   "source": [
    "df1[df1[\"r_hat\"]>1.2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2[df2[\"r_hat\"]>1.2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def trans_flag(behavior,flag):\n",
    "    if behavior == \"rg\":\n",
    "        return flag == 2\n",
    "    elif behavior == \"cheat\":\n",
    "        return flag == 3\n",
    "def class_table(flag,true_flag):\n",
    "    rg_flag = trans_flag(\"rg\",flag).mean(axis=-1).mean(axis=-1)>0.5\n",
    "    cheat_flag = trans_flag(\"cheat\",flag).mean(axis=-1).mean(axis=-1)>0.5\n",
    "    t_rg = trans_flag(\"rg\",true_flag)\n",
    "    t_c = trans_flag(\"cheat\",true_flag)\n",
    "    rg_table = np.zeros((2,2))\n",
    "    cheat_table = np.zeros((2,2))\n",
    "    for i in range(1,3):\n",
    "        for c in range(1,3):\n",
    "            rg_table[i-1,c-1] = ((rg_flag==i)*(t_rg==c)).sum() \n",
    "            cheat_table[i-1,c-1] = ((cheat_flag==i)*(t_c==c)).sum() \n",
    "    return rg_table,cheat_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "m_result = {}\n",
    "for key in data_key:\n",
    "    m_bias = (m_infer_data[key]-t_data[key]).mean()\n",
    "    m_rmse = (np.power(m_infer_data[key]-t_data[key],2)**0.5).mean()\n",
    "    m_result[key+\"_bias\"] = m_bias\n",
    "    m_result[key+\"_rmse\"] = m_rmse\n",
    "u_result = {}\n",
    "for key in col:\n",
    "    u_bias = (u_infer_data[key]-t_data[key]).mean()\n",
    "    u_rmse = (np.power(u_infer_data[key]-t_data[key],2)**0.5).mean()\n",
    "    u_result[key+\"_bias\"] = u_bias\n",
    "    u_result[key+\"_rmse\"] = u_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "x = []\n",
    "m_rmse_l = []\n",
    "u_rmse_l = []\n",
    "m_bias_l = []\n",
    "u_bias_l = []\n",
    "index = [\"m_rmse\",\"u_rmse\",\"m_bias\",\"u_bias\"]\n",
    "for k in col:\n",
    "    x.append(k)\n",
    "    m_rmse_l.append(m_result[k+\"_rmse\"])\n",
    "    u_rmse_l.append(u_result[k+\"_rmse\"])\n",
    "    m_bias_l.append(m_result[k+\"_bias\"])\n",
    "    u_bias_l.append(u_result[k+\"_bias\"])\n",
    "    # print(\"m_rmse\",k,m_result[k+\"_rmse\"])\n",
    "    # print(\"u_rmse\",k,u_result[k+\"_rmse\"])\n",
    "df = pd.DataFrame([m_rmse_l,u_rmse_l,m_bias_l,u_bias_l],columns=col,index=index).T\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "((m_trace[\"att\"].mean(axis=-1).mean(axis=-1)>0.5)==\\\n",
    "student.specific_att).prod(axis=-1).mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for k in col:\n",
    "#     print(\"m_bias\",k,m_result[k+\"_bias\"])\n",
    "#     print(\"u_bias\",k,u_result[k+\"_bias\"])\n",
    "# sns.lineplot(data=df)\n",
    "# help(sns.lineplot)\n",
    "# df[\"index\"]=[i for i in range(12)]\n",
    "plt.figure(figsize = (13.5,3))\n",
    "sns.lineplot(data=df[[\"m_rmse\",\"u_rmse\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (13.5,3))\n",
    "plt.plot([i for i in range(df.shape[0])],np.repeat(0,df.shape[0]))\n",
    "sns.lineplot(data=df[\"m_rmse\"]-df[\"u_rmse\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (13.5,3))\n",
    "plt.plot([i for i in range(df.shape[0])],np.repeat(0,df.shape[0]))\n",
    "sns.lineplot(data=df[[\"m_bias\",\"u_bias\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
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  "kernelspec": {
   "display_name": "Python 3.7.10 64-bit ('mcmc': conda)",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
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